Project Summary: A deep understanding of the genetic and epigenetic regulatory logic that controls early development in hu- mans is essential for uncovering the mechanisms of developmental diseases and designing new protocols for regenerative medicine applications. Although over the years many developmentally important genes, there has not been a systematic understanding of how these genes interact dynamically to create cellular and organismal phenotype. For this purpose, we propose to combine experimental and computational approaches to develop predictive models of early germ layer development from human embryonic stem cell (hESC). In our preliminary work, we generated a single-cell RNA-sequencing (scRNA-seq) dataset of 31,000 hESCs, grown as embryoid bodies (EBs) over a period of 27 days to observe differentiation into diverse cell lineages. We developed and applied a new dimensionality reduction and visualization method called PHATE to this system and discovered that PHATE generates a comprehensive and interpretable picture of differentiation. It captures all branches of early development, including ESCs, neural crest cells and their derivatives, neural progenitors, and cells of the mesoderm and endoderm layers. Building upon these ?ndings, we propose to extend this study to a 60-day time course and rendering PHATE more scal- able to capture differentiation to more mature lineages. Then we propose to integrate scRNA-seq and epigenetic data, by interpolating bulk CHIP-seq measurements on sorted populations to a pseudo single- cell resolution. Finally, in order to understand the gene regulatory logic that guides differentiation along speci?c lineages, we will train a new neural network architecture known as DyMon (dynamics modeling network), to walk through the data-manifold to learn a predictive computational model of germ layer de- velopment in its hidden layers. Thus we will connect gene regulatory logic rewiring with developmental cellular phenotypes and offer insights into reprogramming during this process.